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Abstract

This paper describes a radial basis memory system that is used to model the performance of human participants in a task
of learning to traverse mazes in a virtual environment. The memory model is a multiple-trace system, in which each
event is stored as a separate memory trace. In the modeling of the maze traversal task, the events that are stored as
memories are the perceptions and decisions taken at the intersections of the maze. As the virtual agent traverses the
maze, it makes decisions based upon all of its memories, but those that match best to the current perceptual situation, and
which were successful in the past, have the greatest influence. As the agent carries out repeated attempts to traverse the
same maze, memories of successful decisions accumulate, and performance gradually improves. The system uses only
three free parameters, which most importantly includes adjustments to the standard deviation of the underlying Gaussian
used as the radial basis function. It is demonstrated that adjustments of these parameters can easily result in exact
modeling of the average human performance in the same task, and that variation of the parameters matches the variation
in human performance. We conclude that human memory interaction that does not involve conscious memorization, as
in learning navigation routes, may be much more primitive and simply explained than has been previously thought.

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Journal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews